The object detection technology applied to video surveillance nowadays has two common ways: one is to detect the predefined specific objects in the image and the other one is to subtract background image from a current image to obtain foreground.
The object detection based on background subtraction needs to collect a great deal of background image and then statistically analyze the collected image to produce static and effective background image. After that, the background image will continuously be updated by every current image to automatically vary with time.
Nevertheless, when rotary video capturing devices, such as pan-tilt surveillance cameras or pan-tilt-zoom surveillance cameras, patrol in different view positions, the field of view of captured images will change. When the background image of the field of view in the current view position is not learnt and converged to become stable and the rotary video capturing device moves to next view position, the object detection will output an invalid result or continue outputting wrong results.